Instructions to use kittinan/exercise-feedback-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kittinan/exercise-feedback-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kittinan/exercise-feedback-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kittinan/exercise-feedback-classification") model = AutoModelForSequenceClassification.from_pretrained("kittinan/exercise-feedback-classification") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Reddit exercise feedback classification
|
| 2 |
+
|
| 3 |
+
Model to classify Reddit's comments for exercise feedback. Current classes are good, correction, bad posture, not informative. If you want to use it locally,
|
| 4 |
+
|
| 5 |
+
### Usage:
|
| 6 |
+
```py
|
| 7 |
+
from transformers import pipeline
|
| 8 |
+
classifier = pipeline("text-classification", "kittinan/exercise-feedback-classification")
|
| 9 |
+
classifier("search for alan thrall deadlift video he will explain basic ques")
|
| 10 |
+
#[{'label': 'LABEL_1', 'score': 0.9998193979263306}]
|
| 11 |
+
```
|